Analyzing digital holographic microscopy data for hematology applications

ABSTRACT

A method for analyzing digital holographic microscopy (DHM) data for hematology applications includes receiving a DHM image acquired using a digital holographic microscopy system and identifying one or more erythrocytes in the DHM image. For each respective erythrocyte included in the one or more erythrocytes, a cell thickness value for the respective erythrocyte using a parametric model is estimated, and a cell volume value is calculated for the respective erythrocyte using the cell thickness value.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application Ser.No. 62/012,636 filed Jun. 16, 2014, which is incorporated herein byreference in its entirety.

TECHNICAL FIELD

The present disclosure relates generally to analyzing digitalholographic microscopy (DHM) for hematology applications. The varioussystems, methods, and apparatuses described herein may be applied, forexample, red blood cell (RBC) volume measurement and white blood cell(WBC) differential (cell type classification) applications.

BACKGROUND

Digital holographic microscopy (DHM), also known as interference phasemicroscopy, is an imaging technology that provides the ability toquantitatively track sub-nanometric optical thickness changes intransparent specimens. Unlike traditional digital microscopy, in whichonly intensity (amplitude) information about a specimen is captured, DHMcaptures both phase and intensity. The phase information, captured as ahologram, can be used to reconstruct extended morphological information(such as depth and surface characteristics) about the specimen using acomputer algorithm. Modern DHM implementations offer several additionalbenefits, such as fast scanning/data acquisition speed, low noise, highresolution and the potential for label-free sample acquisition.

Conventional cellular analysis techniques such as volume measurement andclassification rely on two-dimensional cellular images that lacktopographical information. Thus, while these techniques may analyze acell based on information such as intensity, their accuracy is limiteddue to a lack of knowledge of the size and shape of the cell.Accordingly, it is desired to provide cellular analysis techniques whichare applicable to imaging modalities such as DHM that provide moredetailed information regarding cellular structure.

SUMMARY

Embodiments of the present invention address and overcome one or more ofthe above shortcomings and drawbacks, by providing methods, systems, andapparatuses related to analyzing digital holographic microscopy (DHM)for hematology applications. Additionally, as explained in furtherdetail in the disclosure, the technology described herein may be appliedto other clinical applications as well.

The ability of DHM to achieve high-resolution, wide field imaging withextended depth and morphological information in a potentially label-freemanner positions the technology for use in several clinicalapplications. For example, in the area of hematology DHM may be used forred blood cell (RBC) volume measurement, white blood cell (WBC)differential (cell type classification). For urine sediment analysis DHMallows for scanning a microfluidic sample in layers to reconstruct thesediment (possibly without waiting for sedimentation); improving theclassification accuracy of sediment constituents. DHM may also be usedfor tissue pathology applications through utilization of extendedmorphology/contrast of DHM (e.g. to discriminate cancerous from healthycells, in fresh tissue, without labeling). Similarly, for rare celldetection applications may utilize extended morphology/contrast of DHM(e.g. to differentiate rare cells such as circulating tumor/epithelialcells, stem cells, infected cells, etc.).

According to some embodiments of the present invention, a method foranalyzing digital holographic microscopy (DHM) data for hematologyapplications includes receiving a DHM image acquired using a digitalholographic microscopy system and identifying one or more erythrocytesin the DHM image. Next, for each respective erythrocyte included in theone or more erythrocytes, a cell thickness value is estimated for therespective erythrocyte using a parametric model and a cell volume valueis calculated for the respective erythrocyte using the cell thicknessvalue. In some embodiments, the method further comprising performing acomplete blood cell (CBC) test using the cell thickness value and thecell volume value associated with each respective erythrocyte.

Various enhancements, modifications, or additions may be added to theaforementioned method in different embodiments of the present invention.For example, in some embodiments, the method further comprisesperforming a segmentation on the DHM image to generate a segmentationmask including boundaries for the one or more erythrocytes. Thissegmentation may be performed, for example, by minimizing a piecewiseconstant energy functional in a combinatorial optimization framework.Following the segmentation, a connected component analysis may beperformed on the segmentation mask to label the one or moreerythrocytes. In some embodiments, the parametric model represents eachof the one or more erythrocytes using Cassini ovals. In otherembodiments, the parametric model represents each of the one or moreerythrocytes using a cell diameter value, a minimum dimple thicknessvalue, a maximum thickness value and a circle diameter valuerepresentative of maximum cell thickness. The parameters used in theparametric model may be determined, for example, by minimizing a sumsquared error between a depth map estimated from the parametric modeland a depth observed from the DHM image.

According to another aspect of the present invention, as described insome embodiments, an article of manufacture for analyzing DHM data forhematology applications comprise a non-transitory, tangiblecomputer-readable medium holding computer-executable instructions forperforming the aforementioned method. This article of manufacture mayfurther include instructions for any of the additional featuresdiscussed above with respect to the aforementioned method.

A system for analyzing DHM data for hematology applications comprises anetworking component and a modeling processor. The networking componentis configured to communicate with using a DHM system to retrieve a DHMimage. The modeling processor is configured to identify one or moreerythrocytes in the received DHM image. The modeling processor isfurther configured to estimate a cell thickness value (using aparametric model) and calculate a cell volume for each respectiveerythrocyte included in the one or more erythrocytes.

Additional features and advantages of the invention will be madeapparent from the following detailed description of illustrativeembodiments that proceeds with reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other aspects of the present invention are bestunderstood from the following detailed description when read inconnection with the accompanying drawings. For the purpose ofillustrating the invention, there is shown in the drawings embodimentsthat are presently preferred, it being understood, however, that theinvention is not limited to the specific instrumentalities disclosed.Included in the drawings are the following Figures:

FIG. 1 provides an illustration of a framework for processing DHM imagesfor erythrocyte volume computation, according to some embodiments;

FIG. 2 provides an example illustration of segmentation of erythrocytesin optical path difference DHM (OPD-DHM) images, as may performed insome embodiments;

FIG. 3 shows a geometry model of a normal erythrocyte, as may beutilized in some embodiments;

FIG. 4 provides an example of the parametric modeling of theerythrocytes which illustrates the regularization;

FIG. 5 provides an illustration of a process for calculating cellvolume, according to some embodiments;

FIG. 6 provides an illustration of a pre-processing framework forclassifying white blood cells, as it may be applied in some embodiments;

FIG. 7 shows a sample of the white blood cells used in training andtesting, according to some embodiments;

FIG. 8A provides a table showing an example of a data set that may beused for training and testing for each cell type, as may be used in someembodiments;

FIG. 8B shows pairwise classification results obtained using textonfeatures and KNN classifier for this sample case, as may be obtainedaccording to some embodiments;

FIG. 9 provides an example of local image feature sampling, as it may beapplied in some embodiments;

FIG. 10 shows a complete binary search tree structure which represents avocabulary dictionary structures;

FIG. 11 provides a visualization of the workflow for the transformationfrom local image features (dense SIFT descriptor) to a global imagefeatures (vocabulary histogram), as may be applied in some embodiments;

FIG. 12 shows Pairwise Classification results using SIFT and SVMclassification, obtained according to some embodiments;

FIG. 13 shows the structure of a feed-forward neural network with onehidden layer that may be utilized in some embodiments;

FIG. 14 shows a GUI which provides for results visualization andfacilitates user interaction and correction, according to someembodiments;

FIG. 15 illustrates an exemplary computing environment within whichembodiments of the invention may be implemented

DETAILED DESCRIPTION

The following disclosure describes the present invention according toseveral embodiments directed at methods, systems, and apparatusesrelated to analyzing digital holographic microscopy (DHM) for hematologyand other clinical applications. Briefly, the techniques discussedherein comprise of three principle ideas that may be applied indifferent configurations in the various embodiments described herein.First, in some embodiments, red blood cell (RBC) volume estimation isperformed using parametric modeling of erythrocytes to regularize theraw DHM data to match physical models of the erythrocytes, in an attemptto eliminate any distortion caused during the image acquisition process.Second, in some embodiments, label-free differentiation of white bloodcells (WBCs) captured by DHM is performed using a machine learningalgorithm which extract characteristic topological changes from a set oftraining samples DHM images for each sub-type of WBCs (namely,monocytes, neutrophils, basophils, lymphocytes, eosinophils). Themachine learning algorithm then classifies a new cell into one of thecategories automatically based on the learnt DHM image based features.Third, in some embodiments, “digital staining” or “pseudo-coloring” ofthe classified DHM images of the cells is performed to resembleconventional staining techniques. The digital staining method describedherein uses a matched pair of DHM and stained images of a set of cells,and a regression function is learnt to map the DHM image pixel(representing the topology of the cell constituents to a RGB colorscheme of conventional staining techniques).

FIG. 1 provides an illustration of a framework 100 for processing DHMimages for erythrocyte volume computation, according to someembodiments. Briefly, a DHM System 105 is used to acquire one or moreinput images. The Digital Holographic Microscopy System 105 may be anysystem known in the art capable of acquiring DHM images. An ImageSegmentation Component 110 performs segmentation of the erythrocytes inthe acquired input images. Next, a Parametric Modeling Component 115develops a model of the erythrocytes (e.g., using Cassini ovals). Then,a Parametric Modeling Component 120 calculates thickness information ofeach erythrocyte which, in turn, can be used to determine theerythrocyte's volume.

Various segmentation techniques may be applied to the input image tosegment the erythrocytes. For example, in some embodiments, thesegmentation problem performed by the Image Segmentation Component 110is formulated as an energy minimization problem that minimizes thepiecewise constant Mumford-Shah energy functional in a combinatorialoptimization framework. The segmentation energy can be described asfollows. Given an image u and an image of interest u: Ω→R, where Ω is anopen bounded subset in R2 that comprises several connected componentsΩ_(i) and bounded by a closed boundary C=∂Ω, find a piecewise constantapproximation such that u is constant within the components Ω_(i) andsharp in the transition across the boundary C. This is formulated as:

F(x1,x2, . . . ,xn)=μΣ_(epgεE) w _(pq) |x _(p) −x _(p) |u(P)−c ₁|² x_(p)+Σ_(p) −c ₁|²(1−x _(p))   (1)

where x_(p) is a binary variable that indicates whether a particularpixel p=(x, y) belongs to the cell or to the background. A binaryvariable x_(p) for each pixel p=(x, y)εΩ is set such that

$\begin{matrix}{x_{p} = \{ \begin{matrix}1 & {{{if}\mspace{14mu} p} \in {\bigcup_{i}\Omega_{i}}} \\{0,} & {otherwise}\end{matrix} } & (2)\end{matrix}$

The constants c₁ and c₂ represent the piecewise constant approximationof the input DHM image, w_(pq) represents the weights of the Euclideanlength regularization, and μ is a weighting coefficient that defines thecontribution of the length regularization to the segmentation energy. Arelatively high value for mu may be used to avoid separating the nucleusfrom the rest of the cell.

FIG. 2 provides an example illustration of Segmentation of erythrocytesin optical path difference DHM (OPD-DHM) images. Image 205 shows theinput image, while image 210 shows the delineation (represented by adotted line) of the erythrocyte boundaries is imposed on the image 205.

A normal erythrocyte is generally shaped as a biconcave disk to achievea large surface area-to-volume ratio. For example, FIG. 3 shows ageometry model 300 of a normal erythrocyte. There are four mainparameters that affect the shape of the cell (shown in FIG. 3): thediameter of the cell (D), the minimum dimple thickness (t), the maximumthickness (h) and the diameter of the circle that determines the maximumthickness (d).

Using the geometric understanding presented in FIG. 3, there are severalbiconcave models for the erythrocyte surface representation that may beapplied by Parametric Modeling Component 120 to characterize the shapeand geometry of each erythrocyte. For example, some embodiments utilizethe Evans-Fung Model technique generally known in the art which utilizesthe following equation:

$\begin{matrix}{{z(\rho)} = {\pm {\sqrt{1 - ( \frac{\rho}{R} )^{2\;}}\lbrack {{c\; 0} + {c\; 1( \frac{\rho}{R} )^{2}} + {c\; 2( \frac{\rho}{R} )^{4}}} \rbrack}}} & (3)\end{matrix}$

where R is the radius of the cell (R=D/2) and ρ is the horizontaldistance from the center of the cell. To estimate the model parametersc0, c1 and c2 we minimize the sum squared error between the depth map asestimated from the parametric model and the depth observed from the DHM.The sum squared error of the thickness profile is expressed as:

$\begin{matrix}{{SSE}_{thickness} = {\sum_{p}( {{z(\rho)} - \frac{u(\rho)}{2}} )^{2}}} & (4)\end{matrix}$

In some embodiments, the parametric modeling of the cell regularizes thecell surface to match the physical models of the erythrocytes andeliminate any distortion caused during the image acquisition process.FIG. 4 provides an example of the parametric modeling of theerythrocytes which illustrates the regularization. In Image 405, thecell surface is depicted as observed from the DHM image. Image 410 showsthe cell surface as characterized by the Evans—Fung model.

After performing the segmentation and the parametric modeling for eachcell, the volume of each RBC may be computed. The RBC volume may then beused as a clinical measurement in and of itself, or it may be used toderive additional clinically significant values. For example, thecalculation of RBC volume may be used in determining mean cell volume(MCV) which, in turn, is a critical parameter in a complete blood count(CBC).

FIG. 5 provides an illustration of a process 500 for calculating cellvolume, according to some embodiments. At step 505, an OPD-DHM image oferythrocytes is received. Next, at step 510, the DHM image is segmentedto extract all the erythrocytes from the background. Segmentation may beperformed, for example, using the combinatorial piece wise constantMumford-Shah energy functional or any similar technique generally knownin art. At step 515, connected component analysis is applied to thesegmentation mask to label the independent erythrocytes.

Continuing with references to FIG. 5, at steps 520-530 a measurementprocess is performed for each erythrocyte. At step 520, the cell surfaceis approximated, for example, using the Evans-Fung technique. The sumsquared differences in Equation 4 are optimized and the model parametersC0, C1 and C2 are computed. Next, at step 525, the model is used toestimate the updated cell thickness z(ρ) using Equation 3. Then, at step530, the updated cell thickness is used to calculate the cell volume.The output of the process 500 is a volume for each erythrocyte.

The process 500 can be extended to provide the mean hemoglobin contentof the cell as well. For this, a conventional hematology analyzer may beused for calibration of mean cell volume and conversion of opticaldensity map based volume to the average hemoglobin content.

Differential blood tests measure the percentage of each type of whiteblood cell type in a blood cell sample. Identifying each type of whiteblood cell is a crucial preliminary step to blood differential analysisthat is used to diagnose diseases such as infections, anemia andLeukemia. To address this step, the system described herein may beapplied to differentiate among the different white blood cell types.Specifically, the described system aims to differentiate five differenttypes of white blood cells, namely: monocytes, basophils, neutrophils,eosinophils and lymphocytes. Briefly, a pipeline of processing the whiteblood cells comprises the following three steps. The first step ispre-processing where various types of white blood cells are identifiedand isolated. The second step is training where a classifier is trainedfrom the extracted cells. The third step is classification where aclassifier is used to categorize unseen cells. Each of these steps willnow be discussed in detail.

FIG. 6 provides an illustration of a pre-processing framework 600 forclassifying white blood cells, as it may be applied in some embodiments.During the pre-processing step, a pipeline is used to prepare thetraining patches for the classifier training step. Initially, athreshold is applied to an input image 605 to capture the bright spots(that are highly likely to be cells). After thresholding, a connectedcomponent analysis is applied to the thresholded image 610. Then, asillustrated in image 615, the component size is calculated for eachconnected component. A component is rejected if its size is below orabove predefined thresholds t1 and t2. The rejected components areexcluded from training and testing. The patches (patch is a rectangularbox including the connected component), including the remainingcomponents are used to train the classifier (e.g., a K-Nearest Neighborclassifier).

Following the pre-processing framework 600, various machine learningalgorithms may be used for performing five part differential on DHMimages of white blood cells. For example, in some embodiments, atexton-based approach is used, where the textural characteristics of DHMimage of cells are used as the main discriminating feature. In otherembodiments, a more data driven approach is employed, where a dictionaryof image patches is learned and used to represent the entire image as ahistogram. The histogram is then used as the main feature forclassification between various cell sub-types. In other embodiments,classification is based on a convolutional neural network with multiplelayers to perform feature extraction, selection, and classification atonce with a multi-layer network. Each of these different approaches isdescribed in detail below.

As is generally understood in the art, image classification algorithmsare trained using a data set of representative images. One example of atraining data set is illustrated in FIG. 7. In this example, cell imagesare labelled in one of four categories: monocyte, basophil, neutrophil,and lymphocytes. It should be noted that this training set is but oneexample of a data that may be used in training the classifier. Forexample, other training sets employed in some embodiments may includeadditional categories (e.g., eosinophils). Additionally (oralternately), the categories could have a finer level of granularity.

In embodiments where the texton-based approach is used forclassification, the extracted patches that include the preprocessed cellimages are used to train a K-Nearest Neighbor (K-NN) classifier. Theclassifier utilized texton based texture features extracted from eachpatch. Textons are the representation of small texture features by acollection of filter bank responses. Texton based texture classifiersuse the histograms of the textons to characterize the texture by thefrequency of the textons in a particular patched as compared to thetextons of the training data set. The texton features are used, inparticular, because it is pathologically proven that the granularity ofcell nuclei differ in the different cell types and this can be capturedusing texture features.

To illustrate the texton-based approach, consider a combination of thetexton based texture feature representation with a simple K-NNclassifier. FIG. 8A provides a table showing an example of a data setthat may be used for training and testing for each cell type. Theclassification rate obtained for this example dataset in all thepairwise classifications varies from 75% to 95%. FIG. 8B shows pairwiseclassification results obtained using texton features and KNN classifierfor this sample case.

To provide a multi-label classifier using the previous pairwiseclassification, one or more pairwise classifiers may be combined usingvoting. Ideally four classifiers should agree on the class label and 6classifiers would provide random labeling. For example, if 10classifiers are denoted as C1 to C10, the final class label L for anunknown sample S can be represented as the mode (most frequent value) ofthe labels of the 10 classifiers. In other words, the majority vote ofthe 10 pairwise classifiers is labeled for the multi-labelclassification problem. Combining all the pairwise classification to onemulti-label classifier yielded a 76.5% correct classification rate forthe example dataset set presented in FIG. 8A.

In some embodiments, a bag of visual words (BOW) approach may be used tosolve this multi-class based image classification problem. In the BOWapproach, the global image features are represented by vectors ofoccurrence counts of visual words (a histogram over a vocabularydictionary based on local image feature). These global image featuresare then used for classification. The pipeline may be divided into threestages: offline vocabulary learning, image classification training andtesting. In the offline vocabulary learning stage, a visual vocabularydictionary may be trained using hierarchical k-means and SIFT(scale-invariant feature transform) descriptor as local image feature.For classification, one against one n-label supporting vector machine(SVM) may be utilized. To avoid an over-fitting problem, two approachesmay be employed. First, each training image may be perturbed at randomdegree. Secondly, SVM parameters may be determined for the kernel usingcross validation on the training data set.

SIFT descriptor describes invariant local image structure and capturinglocal texture information. Dense SIFT descriptors may be computed forevery n_(s) pixels of each image (w×h, where w and h are image width andheight respectively. For example, a 128 dimension SIFT descriptor may beused and, thus, there are about

$( \frac{w}{n_{s}} ) \times ( \frac{h}{n_{s}} ) \times 128$

local image features per image. FIG. 9 provides an example of localimage feature sampling, as it may be applied in some embodiments. Inthis example, each white dot represents a location where 128 dimensionSIFT descriptor is computed.

In some embodiments, a modified vocabulary tree structure is utilized toconstruct a visual vocabulary dictionary. The vocabulary tree defines ahierarchical quantization using a hierarchical k-means clustering. FIG.10 shows a complete binary (k=2) search tree structure which representsa vocabulary dictionary structures. The node 1005 is a visual clustercenter. 2^(nd) leaf nodes are finally used as visual vocabulary words.n_(d) is the depth of the binary tree. In the vocabulary tree learningstage, first the initial k-means algorithm is applied on to the trainingdata (a collection of SIFT descriptors derived from training data set)and then partitioned into 2 groups, where each group comprises SIFTdescriptors closest to the cluster center. This process is thenrecursively applied until tree depth reaches n_(d). In the online stage,a SIFT descriptor (a vector) is passed down the tree by each level viacomparing this feature vector to the 2 cluster centers and choosing theclosest one. The visual word histogram is computed for all the denseSIFT descriptors on each image.

FIG. 11 provides a visualization of the workflow for the transformationfrom local image features (dense SIFT descriptor) to global imagefeatures (vocabulary histogram). The results obtained by combining SIFTfeatures with SVM classification for an example dataset are depicted inFIG. 12. Combining these pairwise classifiers yielded an 84% correctclassification rate for the 5-type classification problem.

In some embodiments, a Deep Learning (DL) architecture is used toperform the 5-type classification. While SVM is mainly formulated forbinary classification, DL is inherently a multi-label classifier. Toillustrate the DL classification technique, the convolutional networkwas directly trained for the multi-label classification problem. 500cells were used for training, 100 for each category and the rest of thecells were used for testing.

For this example DL application, an auto-encoder convolutional neuralnetwork (CNN) was used to train the marginal space learning (MSL)classifier. FIG. 13 shows the structure of a feed-forward neural networkwith one hidden layer (also referred to as an “AE”). Ignoring the biasterm (the nodes labeled as “+1” in FIG. 13), the input and output layershave the same number of nodes. The objective of such auto encoders is tolearn a transfer function between the input layer (features thatrepresent the images) and the output layer (the labels for the image).If the hidden layer has a size equal or larger than the input layer,potentially, an AE may learn an identity transformation. To prevent sucha trivial solution, previously, an AE is set up with a hidden layer withfewer nodes than the input. Recently, denoising auto-encoder (DAE) wasproposed to learn a more meaningful representation of the input. Acertain percentage (e.g., 50%) of input nodes are randomly picked to bedisturbed (e.g., set the value to zero) and the DAE is required toreconstruct the original input vector given a contaminated observation.With DAE, the hidden layer may have more nodes than the input to achievean over-complete representation. After training an AE, the output layeris discarded and another AE is stacked using the activation response ofthe already trained hidden layer as input to the new AE. This processcan be repeated to train and expand a network layer by layer. Afterpre-training, the output of the hidden layers can be treated ashigh-level image features to train a classifier. Alternatively, we canadd one more layer for the target output and the whole network can berefined using back-propagation.

After the classification is done, the various categories of white bloodcells may be presented to the pathologist in a graphical user interface(GUI) for final confirmation. The probability of a given cell belongingto a certain sub-type may be calculated and presented numerically orgraphically to the pathologist during the final check.

FIG. 14 shows a GUI which provides for results visualization andfacilitates user interaction and correction, according to someembodiments. As shown in the FIG. 14, the pathologist will have a chanceto make a modification based on the DHM image of the cells. The resultsof the automatic blood differential is depicted at the top two rows 1405displaying the percentage of each blood cell type and the possiblediagnosis based on these percentages. The bottom part of the FIG. 1410shows how the user can modify the results. The system shows the top cellcandidates for user interactions. These cells are chosen and sortedbased on the difference of the top two probabilities. The systemdisplays the extracted cell (extracted by our preprocessing pipeline)and displays the probability of it belonging to each WBC type. The usercan accept the label or choose a new label by simply marking a checkbox. If the user changes the label, the system may automatically updatethe counts, the percentages and the possible diagnosis, which changesthe top row and displays the new results.

Additionally, in order for pathologist to best be able to review theimages, in some embodiments, a pseudo-colorization scheme is utilizedwhich converts the DHM images into an image with a color pattern similarto cell images stained for conventional staining methodologies such asthe Wright and Giemsa methodologies. For example, Giemsa-Wright stainsuse solutions which include eosin Y, azure B, and methylene blue forstaining. These solutions bind to the constituents of the cellsincluding nucleus and granules differently and thereby provide a morepronounced coloring which is essential for visual inspection of thecells. From a DHM image of a cell, we obtain different optical densitypatterns for the cell constituents and that could be used as a featureto perform colorization. The method that we envision is based on havingmatched pairs of cells which are imaged both with DHM and Giemsastaining respectively. In some embodiments a simple regression functionis used, which can be implemented using a machine learning techniquesuch as a convolutional neural network to map optical density maps fromDHM to the RGB color scheme consistent with Giemsa-Wright stainingprotocol. The regression map could work either on the single pixel orgroups of pixels (i.e., image patches). In addition, a Markov randomfield based regularization may be utilized to make sure that theneighboring pixels with similar optical density will be coloredsimilarly. Aside from Giemsa and Wright stains, other staining protocolscan be learned based on set of matching pairs of cellular images and thestained version can be digitally reproduced from the DHM images.

In some embodiments, the mean cell volume computation for RBC describedherein and/or the WBC five part differential may be used as keymeasurements for a Complete Blood Count (CBC) test. As is understood inthe art, a CBC test evaluates an individual's overall health and may beused in the detection of blood-related disorders such as anemia,infection and leukemia. So, for example, abnormal increases or decreasesin particular WBC counts may be used as an indicator of an underlyingmedical condition that calls for further evaluation.

Various other extensions, enhancements, or other modifications may bemade or added to the techniques described herein to provide additionalfunctionality. For example, in some embodiments, the five partdifferential methodology for white blood cell sub-typing is applied onraw fringe pattern images prior to the reconstruction as well. In orderto increase the overall accuracy of either RBC volume or WBCclassification, multiple images of the same cell could be used and theresults could be averaged. The systems and methods described herein mayalso be applied to clinical applications outside the cell classificationspace. For example, the size calculation and classification of variousobjects based on the methodology described can be extended for urineanalysis applications, where the size of urine sediments are measuredand various kinds are identified by analyzing the DHM images using themethodology described. Additionally, given the latest advancements inDHM technology—particularly reductions in size, complexity andcost—these (and other) applications could be performed within a clinicalenvironment or at the point of care (in a decentralized manner).

FIG. 15 illustrates an exemplary computing environment 1500 within whichembodiments of the invention may be implemented. For example, thiscomputing environment 1500 may be configured to execute one or more ofthe components of the framework 100 for processing DHM imagesillustrated in FIG. 1. Additionally (or alternatively), this computingenvironment 1500 may be configured to perform one or more of theprocesses described herein (e.g., the process 500 for calculating cellvolume shown in FIG. 1. The computing environment 1500 may includecomputer system 1510, which is one example of a computing system uponwhich embodiments of the invention may be implemented. Computers andcomputing environments, such as computer system 1510 and computingenvironment 1500, are known to those of skill in the art and thus aredescribed briefly here.

As shown in FIG. 15, the computer system 1510 may include acommunication mechanism such as a bus 1521 or other communicationmechanism for communicating information within the computer system 1510.The computer system 1510 further includes one or more processors 1520coupled with the bus 1521 for processing the information. The processors1520 may include one or more central processing units (CPUs), graphicalprocessing units (GPUs), or any other processor known in the art.

The computer system 1510 also includes a system memory 1530 coupled tothe bus 1521 for storing information and instructions to be executed byprocessors 1520. The system memory 1530 may include computer readablestorage media in the form of volatile and/or nonvolatile memory, such asread only memory (ROM) 1531 and/or random access memory (RAM) 1532. Thesystem memory RAM 1532 may include other dynamic storage device(s)(e.g., dynamic RAM, static RAM, and synchronous DRAM). The system memoryROM 1531 may include other static storage device(s) (e.g., programmableROM, erasable PROM, and electrically erasable PROM). In addition, thesystem memory 1530 may be used for storing temporary variables or otherintermediate information during the execution of instructions by theprocessors 1520. A basic input/output system (BIOS) 1533 containing thebasic routines that help to transfer information between elements withincomputer system 1510, such as during start-up, may be stored in ROM1531. RAM 1532 may contain data and/or program modules that areimmediately accessible to and/or presently being operated on by theprocessors 1520. System memory 1530 may additionally include, forexample, operating system 1534, application programs 1535, other programmodules 1536 and program data 1537.

The computer system 1510 also includes a disk controller 1540 coupled tothe bus 1521 to control one or more storage devices for storinginformation and instructions, such as a hard disk 1541 and a removablemedia drive 1542 (e.g., floppy disk drive, compact disc drive, tapedrive, and/or solid state drive). The storage devices may be added tothe computer system 1510 using an appropriate device interface (e.g., asmall computer system interface (SCSI), integrated device electronics(IDE), Universal Serial Bus (USB), or FireWire).

The computer system 1510 may also include a display controller 1565coupled to the bus 1521 to control a display 1566, such as a cathode raytube (CRT) or liquid crystal display (LCD), for displaying informationto a computer user. The computer system includes an input interface 1560and one or more input devices, such as a keyboard 1562 and a pointingdevice 1561, for interacting with a computer user and providinginformation to the processor 1520. The pointing device 1561, forexample, may be a mouse, a trackball, or a pointing stick forcommunicating direction information and command selections to theprocessor 1520 and for controlling cursor movement on the display 1566.The display 1566 may provide a touch screen interface which allows inputto supplement or replace the communication of direction information andcommand selections by the pointing device 1561.

The computer system 1510 may perform a portion or all of the processingsteps of embodiments of the invention in response to the processors 1520executing one or more sequences of one or more instructions contained ina memory, such as the system memory 1530. Such instructions may be readinto the system memory 1530 from another computer readable medium, suchas a hard disk 1541 or a removable media drive 1542. The hard disk 1541may contain one or more datastores and data files used by embodiments ofthe present invention. Datastore contents and data files may beencrypted to improve security. The processors 1520 may also be employedin a multi-processing arrangement to execute the one or more sequencesof instructions contained in system memory 1530. In alternativeembodiments, hard-wired circuitry may be used in place of or incombination with software instructions. Thus, embodiments are notlimited to any specific combination of hardware circuitry and software.

As stated above, the computer system 1510 may include at least onecomputer readable medium or memory for holding instructions programmedaccording to embodiments of the invention and for containing datastructures, tables, records, or other data described herein. The term“computer readable medium” as used herein refers to any medium thatparticipates in providing instructions to the processor 1520 forexecution. A computer readable medium may take many forms including, butnot limited to, non-volatile media, volatile media, and transmissionmedia. Non-limiting examples of non-volatile media include opticaldisks, solid state drives, magnetic disks, and magneto-optical disks,such as hard disk 1541 or removable media drive 1542. Non-limitingexamples of volatile media include dynamic memory, such as system memory1530. Non-limiting examples of transmission media include coaxialcables, copper wire, and fiber optics, including the wires that make upthe bus 1521. Transmission media may also take the form of acoustic orlight waves, such as those generated during radio wave and infrared datacommunications.

The computing environment 1500 may further include the computer system1510 operating in a networked environment using logical connections toone or more remote computers, such as remote computer 1580. Remotecomputer 1580 may be a personal computer (laptop or desktop), a mobiledevice, a server, a router, a network PC, a peer device or other commonnetwork node, and typically includes many or all of the elementsdescribed above relative to computer system 1510. When used in anetworking environment, computer system 1510 may include modem 1572 forestablishing communications over a network 1571, such as the Internet.Modem 1572 may be connected to bus 1521 via user network interface 1570,or via another appropriate mechanism.

Network 1571 may be any network or system generally known in the art,including the Internet, an intranet, a local area network (LAN), a widearea network (WAN), a metropolitan area network (MAN), a directconnection or series of connections, a cellular telephone network, orany other network or medium capable of facilitating communicationbetween computer system 1510 and other computers (e.g., remote computer1580). The network 1571 may be wired, wireless or a combination thereof.Wired connections may be implemented using Ethernet, Universal SerialBus (USB), RJ-11 or any other wired connection generally known in theart. Wireless connections may be implemented using Wi-Fi, WiMAX, andBluetooth, infrared, cellular networks, satellite or any other wirelessconnection methodology generally known in the art. Additionally, severalnetworks may work alone or in communication with each other tofacilitate communication in the network 1571.

As one application of the exemplary computing environment 1500 to thetechnology described herein, consider an example system for analyzingDHM data for hematology applications which includes a network component,a modeling processor, and a GUI. The networking component may includenetwork interface 1570 or some combination of hardware and softwareoffering similar functionality. The networking component is configuredto communicate with a DHM system to retrieve DHM images. Thus, in someembodiments, the networking component may include a specializedinterface for communicating with DHM systems. The modeling processor isincluded in a computing system (e.g. computer system 1510) and isconfigured with instructions that enable it to train a classifier forcell types present in cell images extracted from DHM images received viathe networking component. The modeling processor may include additionalfunctionality, as described in this disclosure, to support this task(e.g., segmentation, identifying connected components, etc.). Themodeling processor is further configured to use the classifier todetermine the probability that new cell images belong to one of thetypes used to train the classifier. The GUI may then be presented on adisplay (e.g., display 1566) for review by a user.

The embodiments of the present disclosure may be implemented with anycombination of hardware and software. In addition, the embodiments ofthe present disclosure may be included in an article of manufacture(e.g., one or more computer program products) having, for example,computer-readable, non-transitory media. The media has embodied therein,for instance, computer readable program code for providing andfacilitating the mechanisms of the embodiments of the presentdisclosure. The article of manufacture can be included as part of acomputer system or sold separately.

While various aspects and embodiments have been disclosed herein, otheraspects and embodiments will be apparent to those skilled in the art.The various aspects and embodiments disclosed herein are for purposes ofillustration and are not intended to be limiting, with the true scopeand spirit being indicated by the following claims.

An executable application, as used herein, comprises code or machinereadable instructions for conditioning the processor to implementpredetermined functions, such as those of an operating system, a contextdata acquisition system or other information processing system, forexample, in response to user command or input. An executable procedureis a segment of code or machine readable instruction, sub-routine, orother distinct section of code or portion of an executable applicationfor performing one or more particular processes. These processes mayinclude receiving input data and/or parameters, performing operations onreceived input data and/or performing functions in response to receivedinput parameters, and providing resulting output data and/or parameters.

A graphical user interface (GUI), as used herein, comprises one or moredisplay images, generated by a display processor and enabling userinteraction with a processor or other device and associated dataacquisition and processing functions. The GUI also includes anexecutable procedure or executable application. The executable procedureor executable application conditions the display processor to generatesignals representing the GUI display images. These signals are suppliedto a display device which displays the image for viewing by the user.The processor, under control of an executable procedure or executableapplication, manipulates the GUI display images in response to signalsreceived from the input devices. In this way, the user may interact withthe display image using the input devices, enabling user interactionwith the processor or other device.

The functions and process steps herein may be performed automatically orwholly or partially in response to user command. An activity (includinga step) performed automatically is performed in response to one or moreexecutable instructions or device operation without user directinitiation of the activity.

The system and processes of the figures are not exclusive. Othersystems, processes and menus may be derived in accordance with theprinciples of the invention to accomplish the same objectives. Althoughthis invention has been described with reference to particularembodiments, it is to be understood that the embodiments and variationsshown and described herein are for illustration purposes only.Modifications to the current design may be implemented by those skilledin the art, without departing from the scope of the invention. Asdescribed herein, the various systems, subsystems, agents, managers andprocesses can be implemented using hardware components, softwarecomponents, and/or combinations thereof. No claim element herein is tobe construed under the provisions of 35 U.S.C. 112, sixth paragraph,unless the element is expressly recited using the phrase “means for.”

1. A method for analyzing digital holographic microscopy (DHM) data forhematology applications, the method comprising: receiving a DHM imageacquired using a digital holographic microscopy system; identifying oneor more erythrocytes in the DHM image; for each respective erythrocyteincluded in the one or more erythrocytes, estimating a cell thicknessvalue for the respective erythrocyte using a parametric model, andcalculating a cell volume value for the respective erythrocyte using thecell thickness value.
 2. The method of claim 1, wherein the one or moreerythrocytes in the DHM image are labeled by a process comprising:performing a segmentation on the DHM image to generate a segmentationmask including boundaries for the one or more erythrocytes; andperforming a connected component analysis on the segmentation mask tolabel the one or more erythrocytes.
 3. The method of claim 2, whereinthe segmentation is performed by minimizing a piecewise constant energyfunctional in a combinatorial optimization framework.
 4. The method ofclaim 1, wherein the parametric model represents each of the one or moreerythrocytes using Cassini ovals.
 5. The method of claim 1, wherein theparametric model represents each of the one or more erythrocytes using acell diameter value, a minimum dimple thickness value, a maximumthickness value and a circle diameter value representative of maximumcell thickness.
 6. The method of claim 1, further comprising: performinga complete blood cell (CBC) test using the cell thickness value and thecell volume value associated with each respective erythrocyte includedin the one or more erythrocytes.
 7. The method of claim 1, whereinparameters used in the parametric model are determined by minimizing asum squared error between a depth map estimated from the parametricmodel and a depth observed from the DHM image.
 8. An article ofmanufacture for analyzing digital holographic microscopy (DHM) data forhematology applications, the article of manufacture comprising anon-transitory, tangible computer-readable medium holdingcomputer-executable instructions for performing a method comprising:receiving a DHM image acquired using a digital holographic microscopysystem; identifying one or more erythrocytes in the DHM image; for eachrespective erythrocyte included in the one or more erythrocytes,estimating a cell thickness value for the respective erythrocyte using aparametric model, and calculating a cell volume value for the respectiveerythrocyte using the cell thickness value.
 9. The article ofmanufacture of claim 8, wherein the one or more erythrocytes in the DHMimage are labeled by a process comprising: performing a segmentation onthe DHM image to generate a segmentation mask including boundaries forthe one or more erythrocytes; and performing a connected componentanalysis on the segmentation mask to label the one or more erythrocytes.10. The article of manufacture of claim 9, wherein the segmentation isperformed by minimizing a piecewise constant energy functional in acombinatorial optimization framework.
 11. The article of manufacture ofclaim 8, wherein the parametric model represents each of the one or moreerythrocytes using Cassini ovals.
 12. The article of manufacture ofclaim 8, wherein the parametric model represents each of the one or moreerythrocytes using a cell diameter value, a minimum dimple thicknessvalue, a maximum thickness value and a circle diameter valuerepresentative of maximum cell thickness.
 13. The article of manufactureof claim 8, wherein the method further comprises: performing a completeblood cell (CBC) test using the cell thickness value and the cell volumevalue associated with each respective erythrocyte included in the one ormore erythrocytes.
 14. The article of manufacture of claim 8, whereinparameters used in the parametric model are determined by minimizing asum squared error between a depth map estimated from the parametricmodel and a depth observed from the DHM image.
 15. A system foranalyzing digital holographic microscopy (DHM) data for hematologyapplications, the system comprising: a networking component configuredto communicate with a digital holographic microscopy system to retrievea DHM image; a modeling processor configured to: identify one or moreerythrocytes in the DHM image; for each respective erythrocyte includedin the one or more erythrocytes, estimate a cell thickness value for therespective erythrocyte using a parametric model, and calculate a cellvolume value for the respective erythrocyte using the cell thicknessvalue.